Method and apparatus for inter-channel prediction and transform for point cloud attribute coding

ABSTRACT

A method of interframe point cloud attribute coding is performed by at least one processor and includes coding a first color attribute of a point of a point cloud to obtain a first reconstructed residual, coding a second color attribute of the point to obtain a second reconstructed residual, and determining a quantization index of the second reconstructed residual, based on the first reconstructed residual and the second reconstructed residual. The method further includes updating the second reconstructed residual, based on the quantization index and the first reconstructed residual.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of U. S. application Ser.No. 16/924,790, filed Jul. 9, 2020, which claims priority from U.S.Provisional Patent Application No. 62/872,972, filed on Jul. 11, 2019,in the U.S. Patent and Trademark Office, which is incorporated herein byreference in its entirety.

BACKGROUND 1. Field

Methods and apparatuses consistent with embodiments relate tograph-based point cloud compression (G-PCC), and more particularly, amethod and an apparatus for inter-channel prediction and transform forpoint cloud attribute coding.

2. Description of Related Art

Advanced three-dimensional (3D) representations of the world areenabling more immersive forms of interaction and communication, and alsoallow machines to understand, interpret and navigate our world. 3D pointclouds have emerged as an enabling representation of such information. Anumber of use cases associated with point cloud data have beenidentified, and corresponding requirements for point cloudrepresentation and compression have been developed.

A point cloud is a set of points in a 3D space, each with associatedattributes, e.g., color, material properties, etc. Point clouds can beused to reconstruct an object or a scene as a composition of suchpoints. They can be captured using multiple cameras and depth sensors invarious setups, and may be made up of thousands up to billions of pointsto realistically represent reconstructed scenes.

Compression technologies are needed to reduce the amount of data torepresent a point cloud. As such, technologies are needed for lossycompression of point clouds for use in real-time communications and sixdegrees of freedom (6 DoF) virtual reality. In addition, technology issought for lossless point cloud compression in the context of dynamicmapping for autonomous driving and cultural heritage applications, etc.The Moving Picture Experts Group (MPEG) has started working on astandard to address compression of geometry and attributes such ascolors and reflectance, scalable/progressive coding, coding of sequencesof point clouds captured over time, and random access to subsets of apoint cloud.

FIG. 1A is a diagram illustrating a method of generating levels ofdetail (LoD) in G-PCC.

Referring to FIG. 1A, in current G-PCC attributes coding, an LoD (i.e.,a group) of each 3D point (e.g., P0-P9) is generated based on a distanceof each 3D point, and then attribute values of 3D points in each LoD isencoded by applying prediction in an LoD-based order 110 instead of anoriginal order 105 of the 3D points. For example, an attributes value ofthe 3D point P2 is predicted by calculating a distance-based weightedaverage value of the 3D points P0, P5 and P4 that were encoded ordecoded prior to the 3D point P2.

A current anchor method in G-PCC proceeds as follows.

First, a variability of a neighborhood of a 3D point is computed tocheck how different neighbor values are, and if the variability is lowerthan a threshold, the calculation of the distance-based weighted averageprediction is conducted by predicting attribute values(a_(i))_(i∈0 . . . k−1), using a linear interpolation process based ondistances of nearest neighbors of a current point i. Let

_(i) be a set of k-nearest neighbors of the current point i, let (ã_(j)

be their decoded/reconstructed attribute values and let (δ_(j)

be their distances to the current point i. A predicted attribute valueâ_(i) is then given by:

$\begin{matrix}{{\hat{a}}_{i} = {{Round}\mspace{11mu}{\left( {\frac{1}{k}{\sum_{j \in \aleph_{t}}{\frac{\frac{1}{\delta_{j}^{2}}}{\sum_{j \in \aleph_{t}}\frac{1}{\delta_{j}^{2}}}{\overset{\sim}{a}}_{j}}}} \right).}}} & (1)\end{matrix}$

Note that geometric locations of all point clouds are already availablewhen attributes are coded. In addition, the neighboring points togetherwith their reconstructed attribute values are available both at anencoder and a decoder as a k-dimensional tree structure that is used tofacilitate a nearest neighbor search for each point in an identicalmanner.

Second, if the variability is higher than the threshold, arate-distortion optimized (RDO) predictor selection is performed.Multiple predictor candidates or candidate predicted values are createdbased on a result of a neighbor point search in generating LoD. Forexample, when the attributes value of the 3D point P2 is encoded byusing prediction, a weighted average value of distances from the 3Dpoint P2 to respectively the 3D points P0, P5 and P4 is set to apredictor index equal to 0. Then, a distance from the 3D point P2 to thenearest neighbor point P4 is set to a predictor index equal to 1.Moreover, distances from the 3D point P2 to respectively the nextnearest neighbor points P5 and P0 are set to predictor indices equal to2 and 3, as shown in Table 1 below.

TABLE 1 Sample of predictor candidate for attributes coding Predictorindex Predicted value 0 average 1 P4 (1^(st) nearest point) 2 P5 (2^(nd)nearest point) 3 P0 (3^(rd) nearest point)

After creating predictor candidates, a best predictor is selected byapplying a rate-distortion optimization procedure, and then, a selectedpredictor index is mapped to a truncated unary (TU) code, bins of whichwill be arithmetically encoded. Note that a shorter TU code will beassigned to a smaller predictor index in Table 1.

A maximum number of predictor candidates MaxNumCand is defined and isencoded into an attributes header. In the current implementation, themaximum number of predictor candidates MaxNumCand is set to equal tonumberOfNearestNeighborsInPrediction+1 and is used in encoding anddecoding predictor indices with a truncated unary binarization.

A lifting transform for attribute coding in G-PCC builds on top of apredicting transform described above. A main difference between theprediction scheme and the lifting scheme is the introduction of anupdate operator.

FIG. 1B is a diagram of an architecture for P/U(Prediction/Update)-lifting in G-PCC. To facilitate prediction andupdate steps in lifting, one has to split a signal into two sets ofhigh-correlation at each stage of decomposition. In the lifting schemein G-PCC, the splitting is performed by leveraging an LoD structure inwhich such high-correlation is expected among levels and each level isconstructed by a nearest neighbor search to organize non-uniform pointclouds into a structured data. A P/U decomposition step at a level Nresults in a detail signal D(N−1) and an approximation signal A(N−1),which is further decomposed into D(N−2) and A(N−2). This step isrepeatedly applied until a base layer approximation signal A(1) isobtained.

Consequently, instead of coding an input attribute signal itself thatconsists of LOD(N), . . . , LOD(1), one ends up coding D(N−1), D(N−2), .. . , D(1), A(1) in the lifting scheme. Note that application ofefficient P/U steps often leads to sparse sub-bands “coefficients” inD(N−1), . . . , D(1), thereby providing a transform coding gainadvantage.

Currently, a distance-based weighted average prediction described abovefor the predicting transform is used for a prediction step in thelifting as an anchor method in G-PCC.

For point cloud attributes such as color, there may be significantredundancy among channels. To improve coding efficiency, a color spaceconversion may be performed as a pre-/post-processing step. One problemof color space conversion as a pre-/post-processing step is that it maylack orthonormality, and optimizing the codec performance in theconverted color space may not necessarily translate into a good qualityin the original space. In addition, lossless color transforms may tendto have extended bit-depths especially when one tries to approximate thenon-integer color transform with a good precision. This can be an issuedepending on the implementation constraints in many practical systems.

SUMMARY

According to embodiments, a method of interframe point cloud attributecoding is performed by at least one processor and includes coding afirst color attribute of a point of a point cloud to obtain a firstreconstructed residual, coding a second color attribute of the point toobtain a second reconstructed residual, and determining a quantizationindex of the second reconstructed residual, based on the firstreconstructed residual and the second reconstructed residual. The methodfurther includes updating the second reconstructed residual, based onthe quantization index and the first reconstructed residual.

According to embodiments, an apparatus for interframe point cloudattribute coding, includes at least one memory configured to storecomputer program code, and at least one processor configured to accessthe at least one memory and operate according to the computer programcode. The computer program code includes coding code configured to causethe at least one processor to code a first color attribute of a point ofa point cloud to obtain a first reconstructed residual, and code asecond color attribute of the point to obtain a second reconstructedresidual, and first determining code configured to cause the at leastone processor to determine a quantization index of the secondreconstructed residual, based on the first reconstructed residual andthe second reconstructed residual. The computer program code furtherincludes updating code configured to cause the at least one processor toupdate the second reconstructed residual, based on the quantizationindex and the first reconstructed residual.

A non-transitory computer-readable storage medium storing instructionsthat cause at least one processor to code a first color attribute of apoint of a point cloud to obtain a first reconstructed residual, code asecond color attribute of the point to obtain a second reconstructedresidual, and determine a quantization index of the second reconstructedresidual, based on the first reconstructed residual and the secondreconstructed residual. The instructions further cause the at least oneprocessor to update the second reconstructed residual, based on thequantization index and the first reconstructed residual.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a diagram illustrating a method of generating LoD in G-PCC.

FIG. 1B is a diagram of an architecture for P/U-lifting in G-PCC.

FIG. 2 is a block diagram of a communication system according toembodiments.

FIG. 3 is a diagram of a placement of a G-PCC compressor and a G-PCCdecompressor in an environment, according to embodiments.

FIG. 4 is a functional block diagram of the G-PCC compressor accordingto embodiments.

FIG. 5 is a functional block diagram of the G-PCC decompressor accordingto embodiments.

FIG. 6 is a flowchart illustrating a method of inter-channel predictionand transform for point cloud attribute coding, according toembodiments.

FIG. 7 is a block diagram of an apparatus for inter-channel predictionand transform for point cloud attribute coding, according toembodiments.

FIG. 8 is a diagram of a computer system suitable for implementingembodiments.

DETAILED DESCRIPTION

Embodiments described herein provide a method and an apparatus forinter-channel prediction and transform for point cloud attribute coding.The method and apparatus efficiently perform inter-channel decorrelationfor compression efficiency. The method and apparatus pertain to acurrent G-PCC predictor design described with respect to FIG. 1A. Themethod and apparatus can be applied to similar codecs designed for pointclouds.

FIG. 2 is a block diagram of a communication system 200 according toembodiments. The communication system 200 may include at least twoterminals 210 and 220 interconnected via a network 250. Forunidirectional transmission of data, a first terminal 210 may code pointcloud data at a local location for transmission to a second terminal 220via the network 250. The second terminal 220 may receive the coded pointcloud data of the first terminal 210 from the network 250, decode thecoded point cloud data and display the decoded point cloud data.Unidirectional data transmission may be common in media servingapplications and the like.

FIG. 2 further illustrates a second pair of terminals 230 and 240provided to support bidirectional transmission of coded point cloud datathat may occur, for example, during videoconferencing. For bidirectionaltransmission of data, each terminal 230 or 240 may code point cloud datacaptured at a local location for transmission to the other terminal viathe network 250. Each terminal 230 or 240 also may receive the codedpoint cloud data transmitted by the other terminal, may decode the codedpoint cloud data and may display the decoded point cloud data at a localdisplay device.

In FIG. 2, the terminals 210-240 may be illustrated as servers, personalcomputers and smartphones, but principles of the embodiments are not solimited. The embodiments find application with laptop computers, tabletcomputers, media players and/or dedicated video conferencing equipment.The network 250 represents any number of networks that convey codedpoint cloud data among the terminals 210-240, including for examplewireline and/or wireless communication networks. The communicationnetwork 250 may exchange data in circuit-switched and/or packet-switchedchannels. Representative networks include telecommunications networks,local area networks, wide area networks and/or the Internet. For thepurposes of the present discussion, an architecture and topology of thenetwork 250 may be immaterial to an operation of the embodiments unlessexplained herein below.

FIG. 3 is a diagram of a placement of a G-PCC compressor 303 and a G-PCCdecompressor 310 in an environment, according to embodiments. Thedisclosed subject matter can be equally applicable to other point cloudenabled applications, including, for example, video conferencing,digital TV, storing of compressed point cloud data on digital mediaincluding CD, DVD, memory stick and the like, and so on.

A streaming system 300 may include a capture subsystem 313 that caninclude a point cloud source 301, for example a digital camera,creating, for example, uncompressed point cloud data 302. The pointcloud data 302 having a higher data volume can be processed by the G-PCCcompressor 303 coupled to the point cloud source 301. The G-PCCcompressor 303 can include hardware, software, or a combination thereofto enable or implement aspects of the disclosed subject matter asdescribed in more detail below. Encoded point cloud data 304 having alower data volume can be stored on a streaming server 305 for futureuse. One or more streaming clients 306 and 308 can access the streamingserver 305 to retrieve copies 307 and 309 of the encoded point clouddata 304. A client 306 can include the G-PCC decompressor 310, whichdecodes an incoming copy 307 of the encoded point cloud data and createsoutgoing point cloud data 311 that can be rendered on a display 312 orother rendering devices (not depicted). In some streaming systems, theencoded point cloud data 304, 307 and 309 can be encoded according tovideo coding/compression standards. Examples of those standards includethose being developed by MPEG for G-PCC.

FIG. 4 is a functional block diagram of a G-PCC compressor 303 accordingto embodiments.

As shown in FIG. 4, the G-PCC compressor 303 includes a quantizer 405, apoints removal module 410, an octree encoder 415, an attributes transfermodule 420, an LoD generator 425, a prediction module 430, a quantizer435 and an arithmetic coder 440.

The quantizer 405 receives positions of points in an input point cloud.The positions may be (x,y,z)-coordinates. The quantizer 405 furtherquantizes the received positions, using, e.g., a scaling algorithmand/or a shifting algorithm.

The points removal module 410 receives the quantized positions from thequantizer 405, and removes or filters duplicate positions from thereceived quantized positions.

The octree encoder 415 receives the filtered positions from the pointsremoval module 410, and encodes the received filtered positions intooccupancy symbols of an octree representing the input point cloud, usingan octree encoding algorithm. A bounding box of the input point cloudcorresponding to the octree may be any 3D shape, e.g., a cube.

The octree encoder 415 further reorders the received filtered positions,based on the encoding of the filtered positions.

The attributes transfer module 420 receives attributes of points in theinput point cloud. The attributes may include, e.g., a color or RGBvalue and/or a reflectance of each point. The attributes transfer module420 further receives the reordered positions from the octree encoder415.

The attributes transfer module 420 further updates the receivedattributes, based on the received reordered positions. For example, theattributes transfer module 420 may perform one or more amongpre-processing algorithms on the received attributes, the pre-processingalgorithms including, for example, weighting and averaging the receivedattributes and interpolation of additional attributes from the receivedattributes. The attributes transfer module 420 further transfers theupdated attributes to the prediction module 430.

The LoD generator 425 receives the reordered positions from the octreeencoder 415, and obtains an LoD of each of the points corresponding tothe received reordered positions. Each LoD may be considered to be agroup of the points, and may be obtained based on a distance of each ofthe points. For example, as shown in FIG. 1A, points P0, P5, P4 and P2may be in an LoD LOD0, points P0, P5, P4, P2, P1, P6 and P3 may be in anLoD LOD1, and points P0, P5, P4, P2, P1, P6, P3, P9, P8 and P7 may be inan LoD LOD2.

The prediction module 430 receives the transferred attributes from theattributes transfer module 420, and receives the obtained LoD of each ofthe points from the LoD generator 425. The prediction module 430 obtainsprediction residuals (values) respectively of the received attributes byapplying a prediction algorithm to the received attributes in an orderbased on the received LoD of each of the points. The predictionalgorithm may include any among various prediction algorithms such as,e.g., interpolation, weighted average calculation, a nearest neighboralgorithm and RDO.

For example, as shown in FIG. 1A, the prediction residuals respectivelyof the received attributes of the points P0, P5, P4 and P2 included inthe LoD LOD0 may be obtained first prior to those of the receivedattributes of the points P1, P6, P3, P9, P8 and P7 included respectivelyin the LoDs LOD1 and LOD2. The prediction residuals of the receivedattributes of the point P2 may be obtained by calculating a distancebased on a weighted average of the points P0, P5 and P4.

The quantizer 435 receives the obtained prediction residuals from theprediction module 430, and quantizes the received predicted residuals,using, e.g., a scaling algorithm and/or a shifting algorithm.

The arithmetic coder 440 receives the occupancy symbols from the octreeencoder 415, and receives the quantized prediction residuals from thequantizer 435. The arithmetic coder 440 performs arithmetic coding onthe received occupancy symbols and quantized predictions residuals toobtain a compressed bitstream. The arithmetic coding may include anyamong various entropy encoding algorithms such as, e.g.,context-adaptive binary arithmetic coding.

FIG. 5 is a functional block diagram of a G-PCC decompressor 310according to embodiments.

As shown in FIG. 5, the G-PCC decompressor 310 includes an arithmeticdecoder 505, an octree decoder 510, an inverse quantizer 515, an LoDgenerator 520, an inverse quantizer 525 and an inverse prediction module530.

The arithmetic decoder 505 receives the compressed bitstream from theG-PCC compressor 303, and performs arithmetic decoding on the receivedcompressed bitstream to obtain the occupancy symbols and the quantizedprediction residuals. The arithmetic decoding may include any amongvarious entropy decoding algorithms such as, e.g., context-adaptivebinary arithmetic decoding.

The octree decoder 510 receives the obtained occupancy symbols from thearithmetic decoder 505, and decodes the received occupancy symbols intothe quantized positions, using an octree decoding algorithm.

The inverse quantizer 515 receives the quantized positions from theoctree decoder 510, and inverse quantizes the received quantizedpositions, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed positions of the points in the input pointcloud.

The LoD generator 520 receives the quantized positions from the octreedecoder 510, and obtains the LoD of each of the points corresponding tothe received quantized positions.

The inverse quantizer 525 receives the obtained quantized predictionresiduals, and inverse quantizes the received quantized predictionresiduals, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed prediction residuals.

The inverse prediction module 530 receives the obtained reconstructedprediction residuals from the inverse quantizer 525, and receives theobtained LoD of each of the points from the LoD generator 520. Theinverse prediction module 530 obtains reconstructed attributesrespectively of the received reconstructed prediction residuals byapplying a prediction algorithm to the received reconstructed predictionresiduals in an order based on the received LoD of each of the points.The prediction algorithm may include any among various predictionalgorithms such as, e.g., interpolation, weighted average calculation, anearest neighbor algorithm and RDO. The reconstructed attributes are ofthe points in the input point cloud.

The method and the apparatus for inter-channel prediction and transformfor point cloud attribute coding will now be described in detail. Such amethod and an apparatus may be implemented in the G-PCC compressor 303described above, namely, the prediction module 430. The method and theapparatus may also be implemented in the G-PCC decompressor 310, namely,the inverse prediction module 530.

A. Inloop Color Residual Transform and Prediction

In embodiments, two methods can avoid the aforementioned problems ofusing a color space transform as a conversion tool for codingefficiency.

One method is to use a YCoCg-R transform as a lossless inloop transformof prediction residuals in Differential Pulse Code Modulation (DPCM)(aka a predicting transform) for G-PCC to decorrelate inter-channeldependency while maintaining near-lossless and lossless reconstruction.Y is a luma value, Co is a chrominance orange value, and Cg is achrominance green value.

Another method is to introduce additional steps of prediction to predictresidual values of other channels in DPCM for G-PCC.

Both of the above-described methods will be detailed as follows.

1. Inloop Color Residual Transform

A YCoCg transform is given as follows:

$\begin{matrix}{\begin{bmatrix}Y \\{Co} \\{Cg}\end{bmatrix} = {{{\begin{bmatrix}{1/4} & {1/2} & {1/4} \\{1/2} & 0 & {{- 1}/2} \\{{- 1}/4} & {1/2} & {{- 1}/4}\end{bmatrix}\begin{bmatrix}R \\G \\B\end{bmatrix}}\begin{bmatrix}R \\G \\B\end{bmatrix}} = {\begin{bmatrix}1 & 1 & {- 1} \\1 & 0 & 1 \\1 & {- 1} & {- 1}\end{bmatrix}\begin{bmatrix}Y \\{Co} \\{Cg}\end{bmatrix}}}} & (2)\end{matrix}$

R is a red value, G is a green value, and B is a blue value.

As a lossless transform that is derived from the YCoCg transform, aforward YCoCg-R transform is given as follows:

Co=R−B  (3)

t=B+(Co>>1)  (4)

Cg=G−t  (5)

Y=t+(Cg>>1)  (6)

t represents an intermediary value used to derive the losslesstransform.

A backward YCoCg-R transform reverses the above process as follows:

t=Y−(Cg>>1)  (7)

G=Cg+t  (8)

B=t−(Co>>1)  (9)

R=B+Co  (10)

When applying the YCoCg-R transform as a residual transform, a signal R,G, and B will be prediction residuals of each channel resulting from perchannel prediction in G-PCC. For example, referring to FIG. 4, theprediction module 430 may obtain the signal R, G, and B as originalprediction residuals of color attributes of points of a point cloud, mayapply the YCoCg-R transform to the obtained original predictionresiduals to obtain a Y, Co, and Cg signal as new prediction residualsof the color attributes, and may output the obtained new predictionresiduals to the quantizer 435.

2. Inter-Channel Color Residual Predictor

Another way of decorrelating a multi-channel signal is to use apredictor. In embodiments, a predictor is used to predict residualsignals of channels so that second order residuals will be quantized andentropy-coded instead of original residuals.

For G-PCC prediction design, a version can be described as follows,where underlined parts are changes to a current G-PCC specification. Anyform of a linear or nonlinear predictor function can be introduced forpossible improvement of this prediction.

Definitions of notations are provided as follows:

X: channel X signal X_pred: predicted channel X signal (obtained fromreconstructed neighbor samples in G-PCC) X_delta_index: quantizationindex of prediction residual of channel X X_delta_recon: reconstructedresidual for channel X X_recon: reconstructed channel X signalX_delta_residual_index: quantization index of residual fromresidual-prediction a. Encoding // G-Channel G_delta_index <-Quantize(G-G_pred) G_delta_recon <- InverseQuantize(G_delta_index)G_recon <- G_pred + G_delta_index EntopyEncode(G_delta_index) //C-Channels (‘C’can be either R or B) C_delta_index <- Quantize(C-C_pred)C_delta_recon <- InverseQuantize(C_delta_index) C_delta_residual index<- Quantize(C_delta_recon − G_delta_recon) C_delta_recon <-InverseQuantize(C_delta_residual_index) + G_delta_recon C_recon <-C_pred + C_delta_recon EntopyEncode(C_delta_residual_index) b. Decoding// G-Channel G_delta_index <- EntropyDecode( ) G_delta_recon <-InverseQuantize(G_delta_index) G_recon <- G_pred + G_delta_recon //C-Channels (‘C’can be either R or B) C_delta_residual_index <-EntropyDecode( ) C_delta_recon <-InverseQuantize(C_delta_residual_index) + G_delta_recon C_recon <-C_pred + C_delta_recon

For example, referring to FIG. 4, the prediction module 430 obtains afirst reconstructed residual for a green color channel (G_delta_recon)and a second reconstructed residual for a red or blue color channel(C_delta_recon). The prediction module 430 then subtracts the obtainedfirst reconstructed residual from the obtained second reconstructedresidual, and quantizes a result of this subtraction, to obtain aquantization index of the second reconstructed residual for the red orblue color channel (C_delta_residual_index). Next, the prediction module430 inverse quantizes the obtained quantization index, and adds a resultof this inverse quantization to the first reconstructed residual, tore-obtain the second reconstructed residual. The prediction module 430may output the re-obtained second reconstructed residual to thequantizer 435.

Advantages of the inter-channel residual predictor for G-PCC and similarpoint cloud codecs may include a change in a decoding process beingminor. Further, the inter-channel residual predictor does not requireany elaborate multi-channel signal model. Unlike other lossless colortransform-based approaches for inter-channel decorrelation, including ainloop residual transform discussed above, the inter-channel residualpredictor can control a fidelity of a signal in an original domain interms of a Hausdorff metric. This is because quantization is performedin an original (RGB color) space.

B. Improving Inloop Color Residual Transform and Prediction for G-PCC

The following embodiments will apply equally to both an inloop colorresidual transform and an inter-channel color residual predictor under acontext of DPCM prediction (aka a predicting transform) in G-PCC. Theinloop color residual transform and the inter-channel color residualpredictor will be called an inter-channel tool.

In embodiments, several methods of a conditional check for decidingwhether to apply an inter-channel tool to a current point of a pointcloud, are described as follows.

In examples, a maximum difference of reconstructed residual values ofthree channels are computed for each of nearest neighbors. Morespecifically, a decoder can track how many neighbors experienced areduced residual magnitude/variance by a set threshold after applying aninter-channel tool. When the three reconstructed residual values arerelatively even (i.e., not reduced in value), chances are decorrelationis not successful and does not need to be performed. This needs abookkeeping of one-flag to indicate a result of such testing per eachpoint, when decoded. Also, a majority-voting may be performed whenmaking a decision.

In other examples, maximum absolute difference values of three channelsfrom neighbor points are compared. When there is a significantdifference in variability of the values among the color channels,chances are that predicting one from another can be difficult, and aninter-channel tool does not need to be used.

In other examples, any measure that can identify inter-channelcorrelation from neighbor samples can be incorporated to determine useof an inter-channel tool.

In other examples, a tile/slice-level (e.g., in tile/slice header) flagor a video/picture-level (e.g., in a Sequence Parameter Set(SPS)/Picture Parameter Set (PPS)) flag can be signaled toenable/disable a color residual transform or a color residual predictor.The signaling can be applied to any grouping of point cloud pixels thatconstitutes a coding-unit.

Referring to FIG. 4, based on any of the above-described flags, theprediction module 430 may determine whether to apply an interloop colorresidual transform to prediction residuals, and/or use an inter-channelcolor residual predictor to obtain new prediction residuals fromoriginal prediction residuals.

FIG. 6 is a flowchart illustrating a method 600 of inter-channelprediction and transform for point cloud attribute coding, according toembodiments. In some implementations, one or more process blocks of FIG.6 may be performed by the G-PCC decompressor 310. In someimplementations, one or more process blocks of FIG. 6 may be performedby another device or a group of devices separate from or including theG-PCC decompressor 310, such as the G-PCC compressor 303.

Referring to FIG. 6, in a first block 610, the method 600 includescoding a first color attribute of a point of a point cloud to obtain afirst reconstructed residual, and coding a second color attribute of thepoint to obtain a second reconstructed residual.

In a second block 620, the method 600 includes determining whether toupdate the second reconstructed residual, based on a flag correspondingto the point. Based on the second reconstructed residual beingdetermined to be updated, the method 600 continues in a third block 630.Otherwise, the method 600 ends.

In the third block 630, the method 600 includes determining aquantization index of the second reconstructed residual, based on thefirst reconstructed residual and the second reconstructed residual.

In a fourth block 640, the method 600 includes updating the secondreconstructed residual, based on the quantization index and the firstreconstructed residual.

The determining the quantization index may include subtracting the firstreconstructed residual from the second reconstructed residual, andquantizing a result of the first reconstructed residual being subtractedfrom the second reconstructed residual, to determine the quantizationindex.

The updating the second reconstructed residual may include inversequantizing the quantization index, and adding the inverse quantizedquantization index to the first reconstructed residual, to update thesecond reconstructed residual.

The method may further include applying a YCoCg-R transform to the firstreconstructed residual and the second reconstructed residual, to updatethe first reconstructed residual and the second reconstructed residual,wherein the YCoCg-R transform is expressed by Equation (2).

The method may further include determining whether to apply the YcoCg-Rtransform, based on the flag. The applying the YcoCg-R transform mayinclude, based on the YcoCg-R transform being determined to be applied,applying the YcoCg-R transform to the first reconstructed residual andthe second reconstructed residual.

The flag may be indicated in a tile or slice level of the point cloud.

Although FIG. 6 shows example blocks of the method 600, in someimplementations, the method 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of the method 600 may be performed in parallel.

Further, the proposed methods may be implemented by processing circuitry(e.g., one or more processors or one or more integrated circuits). In anexample, the one or more processors execute a program that is stored ina non-transitory computer-readable medium to perform one or more of theproposed methods.

FIG. 7 is a block diagram of an apparatus 700 for inter-channelprediction and transform for point cloud attribute coding, according toembodiments.

Referring to FIG. 7, the apparatus 700 includes coding code 710, firstdetermining code 720, and updating code 730.

The coding code 710 is configured to cause at least one processor tocode a first color attribute of a point of a point cloud to obtain afirst reconstructed residual, and code a second color attribute of thepoint to obtain a second reconstructed residual.

The first determining code 720 is configured to cause the at least oneprocessor to determine a quantization index of the second reconstructedresidual, based on the first reconstructed residual and the secondreconstructed residual.

The updating code 730 is configured to cause the at least one processorto update the second reconstructed residual, based on the quantizationindex and the first reconstructed residual.

The first determining code 720 may be further configured to cause the atleast one processor to subtract the first reconstructed residual fromthe second reconstructed residual, and quantize a result of the firstreconstructed residual being subtracted from the second reconstructedresidual, to determine the quantization index.

The updating code 730 may be further configured to cause the at leastone processor to inverse quantize the quantization index, and add theinverse quantized quantization index to the first reconstructedresidual, to update the second reconstructed residual.

The apparatus 700 may further include applying code 740 configured tocause the at least one processor to apply a YcoCg-R transform to thefirst reconstructed residual and the second reconstructed residual, toupdate the first reconstructed residual and the second reconstructedresidual, wherein the YcoCg-R transform is expressed by Equation (2).

The apparatus 700 may further include second determining code 750configured to cause the at least one processor to determine whether toapply the YcoCg-R transform, based on a flag corresponding to the point.The applying code 740 may be further configured to cause the at leastone processor to, based on the YcoCg-R transform being determined to beapplied, apply the YcoCg-R transform to the first reconstructed residualand the second reconstructed residual.

The second determining code 750 may be further configured to cause theat least one processor to determine whether to update the secondreconstructed residual, based on a flag corresponding to the point. Thefirst determining code 720 may be further configured to, based on thesecond reconstructed residual being determined to be updated, determinethe quantization index of the second reconstructed residual, based onthe first reconstructed residual and the second reconstructed residual.

The flag may be indicated in a tile or slice level of the point cloud.

FIG. 8 is a diagram of a computer system 800 suitable for implementingembodiments.

Computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code including instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by computer central processing units (CPUs),Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 8 for the computer system 800 are examplesin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementing theembodiments. Neither should the configuration of the components beinterpreted as having any dependency or requirement relating to any oneor combination of the components illustrated in the embodiments of thecomputer system 800.

The computer system 800 may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): a keyboard 801, a mouse 802, a trackpad 803, atouchscreen 810, a joystick 805, a microphone 806, a scanner 807, and acamera 808.

The computer system 800 may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouchscreen 810 or the joystick 805, but there can also be tactilefeedback devices that do not serve as input devices), audio outputdevices (such as: speakers 809, headphones (not depicted)), visualoutput devices (such as screens 810 to include cathode ray tube (CRT)screens, liquid-crystal display (LCD) screens, plasma screens, organiclight-emitting diode (OLED) screens, each with or without touchscreeninput capability, each with or without tactile feedback capability—someof which may be capable to output two dimensional visual output or morethan three dimensional output through means such as stereographicoutput; virtual-reality glasses (not depicted), holographic displays andsmoke tanks (not depicted)), and printers (not depicted). A graphicsadapter 850 generates and outputs images to the touchscreen 810.

The computer system 800 can also include human accessible storagedevices and their associated media such as optical media including aCD/DVD ROM/RW drive 820 with CD/DVD or the like media 821, a thumb drive822, a removable hard drive or solid state drive 823, legacy magneticmedia such as tape and floppy disc (not depicted), specializedROM/ASIC/PLD based devices such as security dongles (not depicted), andthe like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

The computer system 800 can also include interface(s) to one or morecommunication networks 855. The communication networks 855 can forexample be wireless, wireline, optical. The networks 855 can further belocal, wide-area, metropolitan, vehicular and industrial, real-time,delay-tolerant, and so on. Examples of the networks 855 include localarea networks such as Ethernet, wireless LANs, cellular networks toinclude global systems for mobile communications (GSM), third generation(3G), fourth generation (4G), fifth generation (5G), Long-Term Evolution(LTE), and the like, TV wireline or wireless wide area digital networksto include cable TV, satellite TV, and terrestrial broadcast TV,vehicular and industrial to include CANBus, and so forth. The networks855 commonly require external network interface adapters that attachedto certain general purpose data ports or peripheral buses 849 (such as,for example universal serial bus (USB) ports of the computer system 800;others are commonly integrated into the core of the computer system 800by attachment to a system bus as described below, for example, a networkinterface 854 including an Ethernet interface into a PC computer systemand/or a cellular network interface into a smartphone computer system.Using any of these networks 855, the computer system 800 can communicatewith other entities. Such communication can be uni-directional, receiveonly (for example, broadcast TV), uni-directional send-only (for exampleCANbus to certain CANbus devices), or bi-directional, for example toother computer systems using local or wide area digital networks.Certain protocols and protocol stacks can be used on each of thosenetworks 855 and network interfaces 854 as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces 854 can be attached to a core 840 of thecomputer system 800.

The core 840 can include one or more Central Processing Units (CPU) 841,Graphics Processing Units (GPU) 842, specialized programmable processingunits in the form of Field Programmable Gate Areas (FPGA) 843, hardwareaccelerators 844 for certain tasks, and so forth. These devices, alongwith read-only memory (ROM) 845, random-access memory (RAM) 846,internal mass storage 847 such as internal non-user accessible harddrives, solid-state drives (SSDs), and the like, may be connectedthrough a system bus 848. In some computer systems, the system bus 848can be accessible in the form of one or more physical plugs to enableextensions by additional CPUs, GPU, and the like. The peripheral devicescan be attached either directly to the core's system bus 848, or throughthe peripheral buses 849. Architectures for a peripheral bus includeperipheral component interconnect (PCI), USB, and the like.

The CPUs 841, GPUs 842, FPGAs 843, and hardware accelerators 844 canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in theROM 845 or RAM 846. Transitional data can also be stored in the RAM 846,whereas permanent data can be stored for example, in the internal massstorage 847. Fast storage and retrieve to any of the memory devices canbe enabled through the use of cache memory, that can be closelyassociated with the CPU 841, GPU 842, internal mass storage 847, ROM845, RAM 846, and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of embodiments, or they can be of the kind well known andavailable to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system 800having architecture, and specifically the core 840 can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core 840 that are of non-transitorynature, such as the core-internal mass storage 847 or ROM 845. Thesoftware implementing various embodiments can be stored in such devicesand executed by the core 840. A computer-readable medium can include oneor more memory devices or chips, according to particular needs. Thesoftware can cause the core 840 and specifically the processors therein(including CPU, GPU, FPGA, and the like) to execute particular processesor particular parts of particular processes described herein, includingdefining data structures stored in the RAM 846 and modifying such datastructures according to the processes defined by the software. Inaddition or as an alternative, the computer system can providefunctionality as a result of logic hardwired or otherwise embodied in acircuit (for example: the hardware accelerator 844), which can operatein place of or together with software to execute particular processes orparticular parts of particular processes described herein. Reference tosoftware can encompass logic, and vice versa, where appropriate.Reference to a computer-readable media can encompass a circuit (such asan integrated circuit (IC)) storing software for execution, a circuitembodying logic for execution, or both, where appropriate. Embodimentsencompass any suitable combination of hardware and software.

While this disclosure has described several embodiments, there arealterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods that, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

1. A method of interframe point cloud attribute coding, the method being performed by at least one processor, and the method comprising: coding a first color attribute of a point of a point cloud to obtain a first reconstructed residual; coding a second color attribute of the point to obtain a second reconstructed residual; and determining whether to update the second reconstructed residual, based on a flag corresponding to the point.
 2. The method of claim 1, further comprising: based on the second reconstructed residual being determined to be updated, determining a quantization index of the second reconstructed residual, based on the first reconstructed residual and the second reconstructed residual; and updating the second reconstructed residual, based on the quantization index and the first reconstructed residual.
 3. The method of claim 2, wherein the determining the quantization index comprises: subtracting the first reconstructed residual from the second reconstructed residual; and quantizing a result of the first reconstructed residual being subtracted from the second reconstructed residual, to determine the quantization index.
 4. The method of claim 2, wherein the updating the second reconstructed residual comprises: inverse quantizing the quantization index; and adding the inverse quantized quantization index to the first reconstructed residual, to update the second reconstructed residual.
 5. The method of claim 1, further comprising applying a YCoCg-R transform to the first reconstructed residual and the second reconstructed residual, to update the first reconstructed residual and the second reconstructed residual, wherein the YCoCg-R transform is expressed by: Co=R−B t=B+(Co>>1) Cg=G−t Y=t+(Cg>>1), where Y is a luma value, Co is a chrominance orange value, Cg is a chrominance green value, R is a red value, G is a green value, and B is a blue value of the point.
 6. The method of claim 5, further comprising determining whether to apply the YCoCg-R transform, based on the flag corresponding to the point, wherein the applying the YCoCg-R transform comprises, based on the YCoCg-R transform being determined to be applied, applying the YCoCg-R transform to the first reconstructed residual and the second reconstructed residual.
 7. The method of claim 1, wherein the flag is indicated in a tile or slice level of the point cloud.
 8. An apparatus for interframe point cloud attribute coding, the apparatus comprising: at least one memory configured to store computer program code; and at least one processor configured to access the at least one memory and operate according to the computer program code, the computer program code comprising: coding code configured to cause the at least one processor to code a first color attribute of a point of a point cloud to obtain a first reconstructed residual, and code a second color attribute of the point to obtain a second reconstructed residual; and first determining code configured to cause the at least one processor to determine whether to update the second reconstructed residual, based on a flag corresponding to the point.
 9. The apparatus of claim 8, wherein the computer program code further comprises: second determining code configured to cause the at least one processor to, based on the second reconstructed residual being determined to be updated, determine a quantization index of the second reconstructed residual, based on the first reconstructed residual and the second reconstructed residual; and updating code configured to cause the at least one processor to update the second reconstructed residual, based on the quantization index and the first reconstructed residual.
 10. The apparatus of claim 9, wherein the second determining code is further configured to cause the at least one processor to: subtract the first reconstructed residual from the second reconstructed residual; and quantize a result of the first reconstructed residual being subtracted from the second reconstructed residual, to determine the quantization index.
 11. The apparatus of claim 9, wherein the updating code is further configured to cause the at least one processor to: inverse quantize the quantization index; and add the inverse quantized quantization index to the first reconstructed residual, to update the second reconstructed residual.
 12. The apparatus of claim 8, wherein the computer program code further comprises applying code configured to cause the at least one processor to apply a YCoCg-R transform to the first reconstructed residual and the second reconstructed residual, to update the first reconstructed residual and the second reconstructed residual, wherein the YCoCg-R transform is expressed by: Co=R−B t=B+(Co>>1) Cg=G−t Y=t+(Cg>>1), where Y is a luma value, Co is a chrominance orange value, Cg is a chrominance green value, R is a red value, G is a green value, and B is a blue value of the point.
 13. The apparatus of claim 12, wherein the computer program code further comprises third determining code configured to cause the at least one processor to determine whether to apply the YCoCg-R transform, based on the flag corresponding to the point, wherein the applying code is further configured to cause the at least one processor to, based on the YCoCg-R transform being determined to be applied, apply the YCoCg-R transform to the first reconstructed residual and the second reconstructed residual.
 14. The apparatus of claim 8, wherein the flag is indicated in a tile or slice level of the point cloud.
 15. A non-transitory computer-readable storage medium storing instructions that cause at least one processor to: code a first color attribute of a point of a point cloud to obtain a first reconstructed residual; code a second color attribute of the point to obtain a second reconstructed residual; and determine whether to update the second reconstructed residual, based on a flag corresponding to the point.
 16. The non-transitory computer-readable storage medium of claim 15, wherein the instructions further cause the at least one processor to: determine a quantization index of the second reconstructed residual, based on the first reconstructed residual and the second reconstructed residual; and update the second reconstructed residual, based on the quantization index and the first reconstructed residual.
 17. The non-transitory computer-readable storage medium of claim 16, wherein the instructions further cause the at least one processor to: subtract the first reconstructed residual from the second reconstructed residual; and quantize a result of the first reconstructed residual being subtracted from the second reconstructed residual, to determine the quantization index.
 18. The non-transitory computer-readable storage medium of claim 16, wherein the instructions further cause the at least one processor to: inverse quantize the quantization index; and add the inverse quantized quantization index to the first reconstructed residual, to update the second reconstructed residual.
 19. The non-transitory computer-readable storage medium of claim 15, wherein the instructions further cause the at least one processor to apply a YCoCg-R transform to the first reconstructed residual and the second reconstructed residual, to update the first reconstructed residual and the second reconstructed residual, wherein the YCoCg-R transform is expressed by: Co=R−B t=B+(Co>>1) Cg=G−t Y=t+(Cg>>1), where Y is a luma value, Co is a chrominance orange value, Cg is a chrominance green value, R is a red value, G is a green value, and B is a blue value of the point.
 20. The non-transitory computer-readable storage medium of claim 19, wherein the instructions further cause the at least one processor to: determine whether to apply the YCoCg-R transform, based on the flag corresponding to the point; and based on the YCoCg-R transform being determined to be applied, apply the YCoCg-R transform to the first reconstructed residual and the second reconstructed residual. 